In [ ]:
view_random_images("Angry",8)
!jupyter nbconvert --to html '/content/face_expression_ (1).ipynb'
[NbConvertApp] Converting notebook /content/face_expression_ (1).ipynb to html [NbConvertApp] Writing 8650206 bytes to /content/face_expression_ (1).html
github link- https://github.com/levi-lit/Efficient-Net-Model hugging face - https://huggingface.co/levi15
| Task | Sub Task | Comments |
|---|---|---|
| Data Preprocessing | Scaling and Resizing | Done |
| Image Augmentation | Done | |
| Train and test data handled correctly | Done | |
| Gaussian Blur, Histogram Equalization and Intensity thresholds | Done | |
| Model Trained | Training Time | 2095.284 seconds |
| AUC and Confusion Matrix Computed | Done | |
| Overfitting/Underfitting checked and handled | Done | |
| Empirical Tuning | Interpretability Implemented | Done |
| 1st Round of Tuning | Tried but failed with errors | |
| 2nd Round of Tuning | Done, using the dropout as 0.3 and learning rate as .001 gives the best result. I could only run 1 epoch because it was taking too long. Running more epochs will help to achieve better performance. | |
| Done, implemented L2 regularization and with the same set of dropout and learning rate we get the best result. Similar issue faces, so ran only 1 epoch. | ||
| Deployment | Using Streamlit | for deployment use ngrok and hugging face,Created app.py, index.html, requirements.txt but having an error regarding model configuration |
import tensorflow as tf
import os
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import random
import warnings
warnings.filterwarnings("ignore")
for dirpath, dirnames, filenames in os.walk("/home/archie/Music/ml2/FaceExpressions/dataset"):
print(f"There are {len(filenames)} images in '{dirpath}'.")
def view_random_images(target_class, num_images=8):
target_dir="/home/archie/Music/ml2/FaceExpressions/dataset"
target_folder = os.path.join(target_dir, target_class)
random_images = random.sample(os.listdir(target_folder), num_images)
# Create a subplot to display multiple images
plt.figure(figsize=(12, 6))
for i, image in enumerate(random_images):
plt.subplot(2, 4, i + 1) # 2 rows, 4 columns
img = mpimg.imread(os.path.join(target_folder, image))
plt.imshow(img)
plt.title(f"{target_class} - {i + 1}")
plt.axis("off")
plt.show()
view_random_images("Angry",8)
view_random_images("Surprise",8)
data_dir='/home/archie/Music/ml2/FaceExpressions/dataset'
IMG_SIZE = (224, 224) # image size
total_data= tf.keras.preprocessing.image_dataset_from_directory(directory=data_dir,
image_size=IMG_SIZE,
label_mode="categorical",
batch_size=32)
total_samples = total_data.cardinality().numpy()
train_size = int(0.8 * total_samples)
test_size = total_samples - train_size
# Split the dataset
train_data = total_data.take(train_size)
test_data = total_data.skip(train_size)
print("Number of samples in training set:", train_size)
print("Number of samples in testing set:", test_size)
Number of samples in training set: 386 Number of samples in testing set: 97
total_data.class_names
['Ahegao', 'Angry', 'Happy', 'Neutral', 'Sad', 'Surprise']
checkpoint_path="Expresion/checkpoint.ckpt"
checkpoint_callback=tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
save_weights_only=True,
save_best_only=True,
save_freq="epoch",
verbose=1)
import datetime
def create_tensorboard_callback(dir_name, experiment_name):
log_dir = dir_name + "/" + experiment_name + "/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=log_dir
)
print(f"Saving TensorBoard log files to: {log_dir}")
return tensorboard_callback
from tensorflow.keras import mixed_precision
mixed_precision.set_global_policy('mixed_float16')
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
data_augmentation = Sequential([
layers.RandomFlip("horizontal"),
layers.RandomRotation(0.2),
layers.RandomZoom(0.2),
layers.RandomHeight(0.2),
layers.RandomWidth(0.2),
], name ="data_augmentation")
from tensorflow import keras
from tensorflow.keras import layers
# Setup input shape and base model, freezing the base model layers
input_shape = (224, 224, 3)
base_model = tf.keras.applications.EfficientNetB7(include_top=False)
base_model.trainable = False
# Create input layer
inputs = layers.Input(shape=input_shape, name="input_layer")
# Give base_model the inputs (after augmentation) and don't train it
x = base_model(inputs, training=False)
# Pool output features of the base model
x = layers.GlobalAveragePooling2D(name="global_average_pooling_layer")(x)
# Put a dense layer on as the output
outputs = layers.Dense(6, activation="softmax",dtype='float32', name="output_layer")(x)
#dtype='float32' because we are using mixpresion
# Make a model using the inputs and outputs
model_1 = keras.Model(inputs, outputs)
Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb7_notop.h5 258076736/258076736 [==============================] - 6s 0us/step
from tensorflow.keras.utils import plot_model
plot_model(model_1)
I tried but Augmentation Layer do not increse accuracy despite it decrease in my case,so i removed it
#complile the model
model_1.compile(loss=tf.keras.losses.categorical_crossentropy,
optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
metrics=['accuracy'])
model_1.summary()
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_layer (InputLayer) [(None, 224, 224, 3)] 0
efficientnetb7 (Functional (None, None, None, 2560 64097687
) )
global_average_pooling_lay (None, 2560) 0
er (GlobalAveragePooling2D
)
output_layer (Dense) (None, 6) 15366
=================================================================
Total params: 64113053 (244.57 MB)
Trainable params: 15366 (60.02 KB)
Non-trainable params: 64097687 (244.51 MB)
_________________________________________________________________
history_model_1_efficientB7 = model_1.fit(train_data,
steps_per_epoch=len(train_data),
epochs=10,
batch_size=32,
validation_data=test_data,
validation_steps=len(test_data),
callbacks=[checkpoint_callback]
)
Epoch 1/10 386/386 [==============================] - ETA: 0s - loss: 1.0474 - accuracy: 0.5924 Epoch 1: val_loss improved from inf to 0.91243, saving model to Expresion/checkpoint.ckpt 386/386 [==============================] - 382s 926ms/step - loss: 1.0474 - accuracy: 0.5924 - val_loss: 0.9124 - val_accuracy: 0.6520 Epoch 2/10 386/386 [==============================] - ETA: 0s - loss: 0.8687 - accuracy: 0.6618 Epoch 2: val_loss improved from 0.91243 to 0.85923, saving model to Expresion/checkpoint.ckpt 386/386 [==============================] - 121s 311ms/step - loss: 0.8687 - accuracy: 0.6618 - val_loss: 0.8592 - val_accuracy: 0.6678 Epoch 3/10 386/386 [==============================] - ETA: 0s - loss: 0.8100 - accuracy: 0.6862 Epoch 3: val_loss improved from 0.85923 to 0.83798, saving model to Expresion/checkpoint.ckpt 386/386 [==============================] - 121s 312ms/step - loss: 0.8100 - accuracy: 0.6862 - val_loss: 0.8380 - val_accuracy: 0.6743 Epoch 4/10 386/386 [==============================] - ETA: 0s - loss: 0.7728 - accuracy: 0.7017 Epoch 4: val_loss improved from 0.83798 to 0.81918, saving model to Expresion/checkpoint.ckpt 386/386 [==============================] - 121s 312ms/step - loss: 0.7728 - accuracy: 0.7017 - val_loss: 0.8192 - val_accuracy: 0.6946 Epoch 5/10 386/386 [==============================] - ETA: 0s - loss: 0.7431 - accuracy: 0.7142 Epoch 5: val_loss improved from 0.81918 to 0.81248, saving model to Expresion/checkpoint.ckpt 386/386 [==============================] - 121s 313ms/step - loss: 0.7431 - accuracy: 0.7142 - val_loss: 0.8125 - val_accuracy: 0.6933 Epoch 6/10 386/386 [==============================] - ETA: 0s - loss: 0.7223 - accuracy: 0.7239 Epoch 6: val_loss improved from 0.81248 to 0.80536, saving model to Expresion/checkpoint.ckpt 386/386 [==============================] - 121s 311ms/step - loss: 0.7223 - accuracy: 0.7239 - val_loss: 0.8054 - val_accuracy: 0.6920 Epoch 7/10 386/386 [==============================] - ETA: 0s - loss: 0.7026 - accuracy: 0.7336 Epoch 7: val_loss improved from 0.80536 to 0.79928, saving model to Expresion/checkpoint.ckpt 386/386 [==============================] - 121s 311ms/step - loss: 0.7026 - accuracy: 0.7336 - val_loss: 0.7993 - val_accuracy: 0.6956 Epoch 8/10 386/386 [==============================] - ETA: 0s - loss: 0.6853 - accuracy: 0.7373 Epoch 8: val_loss improved from 0.79928 to 0.79185, saving model to Expresion/checkpoint.ckpt 386/386 [==============================] - 121s 311ms/step - loss: 0.6853 - accuracy: 0.7373 - val_loss: 0.7918 - val_accuracy: 0.7001 Epoch 9/10 386/386 [==============================] - ETA: 0s - loss: 0.6673 - accuracy: 0.7439 Epoch 9: val_loss improved from 0.79185 to 0.78342, saving model to Expresion/checkpoint.ckpt 386/386 [==============================] - 120s 311ms/step - loss: 0.6673 - accuracy: 0.7439 - val_loss: 0.7834 - val_accuracy: 0.7101 Epoch 10/10 386/386 [==============================] - ETA: 0s - loss: 0.6567 - accuracy: 0.7457 Epoch 10: val_loss improved from 0.78342 to 0.77691, saving model to Expresion/checkpoint.ckpt 386/386 [==============================] - 127s 329ms/step - loss: 0.6567 - accuracy: 0.7457 - val_loss: 0.7769 - val_accuracy: 0.7020
# Plot the validation and training data separately
def plot_loss_curves(history):
"""
Returns separate loss curves for training and validation metrics.
"""
loss = history.history['loss']
val_loss = history.history['val_loss']
accuracy = history.history['accuracy']
val_accuracy = history.history['val_accuracy']
epochs = range(len(history.history['loss']))
# Plot loss
plt.plot(epochs, loss, label='training_loss')
plt.plot(epochs, val_loss, label='val_loss')
plt.title('Loss')
plt.xlabel('Epochs')
plt.legend()
# Plot accuracy
plt.figure()
plt.plot(epochs, accuracy, label='training_accuracy')
plt.plot(epochs, val_accuracy, label='val_accuracy')
plt.title('Accuracy')
plt.xlabel('Epochs')
plt.legend();
plot_loss_curves(history_model_1_efficientB7)
Now ploting ROC curve for all the class 6
from sklearn.metrics import roc_curve, auc
from tensorflow.keras.utils import to_categorical
import matplotlib.pyplot as plt
import numpy as np
# First, get the true labels and predictions
y_true = np.concatenate([y for x, y in test_data], axis=0)
y_pred = model_1.predict(test_data)
y_true = np.argmax(y_true, axis=1)
# Calculate ROC curve and ROC area for each class
n_classes = 6 # Number of classes
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
fpr[i], tpr[i], _ = roc_curve(to_categorical(y_true, num_classes=n_classes)[:, i], y_pred[:, i])
roc_auc[i] = auc(fpr[i], tpr[i])
# Plot of a ROC curve for a specific class
for i in range(n_classes):
plt.figure()
plt.plot(fpr[i], tpr[i], label='ROC curve (area = %0.2f)' % roc_auc[i])
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.show()
97/97 [==============================] - 44s 181ms/step
model_1.load_weights(checkpoint_path)
<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x78a926f802b0>
model_1.evaluate(test_data)
97/97 [==============================] - 45s 186ms/step - loss: 0.7860 - accuracy: 0.6988
[0.7860016226768494, 0.6988068222999573]
# Are these layers trainable?
for layer in model_1.layers:
print(layer, layer.trainable)
<keras.src.engine.input_layer.InputLayer object at 0x78adbcf852d0> True <keras.src.engine.functional.Functional object at 0x78ada4e08b20> False <keras.src.layers.pooling.global_average_pooling2d.GlobalAveragePooling2D object at 0x78ae4aafca90> True <keras.src.layers.core.dense.Dense object at 0x78ae7ba96350> True
# Access the base_model layers of model_1
model_1_base_model = model_1.layers[1]
model_1_base_model.name
'efficientnetb7'
# Make all the layers in model_1_base_model trainable
model_1_base_model.trainable = True
# Freeze all layers except for the last 15
for layer in model_1_base_model.layers[:-15]:
layer.trainable = False
# Recompile (we have to recompile our models every time we make a change)
model_1.compile(loss="categorical_crossentropy",
optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), # when fine-tuning you typically want to lower the learning rate by 10x*
metrics=["accuracy"])
print(len(model_1.trainable_variables))
15
# Fine-tune for another 10 epochs
initial_epoch=10
fine_tune_epochs = initial_epoch + 5
# Refit the model (same as model_1 except with more trainable layers)
history_model_1_efficientB7_fineTune = model_1.fit(train_data,
steps_per_epoch=len(train_data),
epochs=fine_tune_epochs,
batch_size=32,
validation_data=test_data,
validation_steps=len(test_data),
initial_epoch=history_model_1_efficientB7.epoch[-1],# start from the previous last epoch
callbacks=[checkpoint_callback]
)
Epoch 10/15 386/386 [==============================] - ETA: 0s - loss: 0.6522 - accuracy: 0.7461 Epoch 10: val_loss improved from 0.77691 to 0.73918, saving model to Expresion/checkpoint.ckpt 386/386 [==============================] - 154s 331ms/step - loss: 0.6522 - accuracy: 0.7461 - val_loss: 0.7392 - val_accuracy: 0.7140 Epoch 11/15 386/386 [==============================] - ETA: 0s - loss: 0.5376 - accuracy: 0.7921 Epoch 11: val_loss improved from 0.73918 to 0.71505, saving model to Expresion/checkpoint.ckpt 386/386 [==============================] - 123s 318ms/step - loss: 0.5376 - accuracy: 0.7921 - val_loss: 0.7150 - val_accuracy: 0.7288 Epoch 12/15 386/386 [==============================] - ETA: 0s - loss: 0.4486 - accuracy: 0.8335 Epoch 12: val_loss did not improve from 0.71505 386/386 [==============================] - 121s 312ms/step - loss: 0.4486 - accuracy: 0.8335 - val_loss: 0.7378 - val_accuracy: 0.7169 Epoch 13/15 386/386 [==============================] - ETA: 0s - loss: 0.3627 - accuracy: 0.8671 Epoch 13: val_loss did not improve from 0.71505 386/386 [==============================] - 121s 312ms/step - loss: 0.3627 - accuracy: 0.8671 - val_loss: 0.7272 - val_accuracy: 0.7417 Epoch 14/15 386/386 [==============================] - ETA: 0s - loss: 0.2904 - accuracy: 0.9000 Epoch 14: val_loss did not improve from 0.71505 386/386 [==============================] - 121s 313ms/step - loss: 0.2904 - accuracy: 0.9000 - val_loss: 0.7788 - val_accuracy: 0.7227 Epoch 15/15 386/386 [==============================] - ETA: 0s - loss: 0.2304 - accuracy: 0.9226 Epoch 15: val_loss did not improve from 0.71505 386/386 [==============================] - 121s 312ms/step - loss: 0.2304 - accuracy: 0.9226 - val_loss: 0.7900 - val_accuracy: 0.7401
model_1.load_weights(checkpoint_path)
<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x78a9290d7610>
model_1.evaluate(test_data)
97/97 [==============================] - 46s 190ms/step - loss: 0.7168 - accuracy: 0.7275
[0.7168375849723816, 0.7275072336196899]
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
import numpy as np
y_pred_labels = np.argmax(y_pred, axis=1) # Convert probabilities to predicted labels
# Compute the confusion matrix
cm = confusion_matrix(y_true, y_pred_labels)
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=total_data.class_names)
# Plot the confusion matrix
disp.plot(cmap=plt.cm.Blues)
plt.show()
from sklearn.metrics import roc_auc_score
# Calculate the ROC-AUC score
roc_auc = roc_auc_score(to_categorical(y_true, num_classes=n_classes), y_pred, multi_class='ovr')
print(f"Average ROC-AUC score: {roc_auc:.2f}")
Average ROC-AUC score: 0.51
checkpoint_path="BigModel/checkpoint.ckpt"
checkpoint_callback=tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
save_weights_only=True,
save_best_only=True,
save_freq="epoch",
verbose=1)
base_model=tf.keras.applications.efficientnet_v2.EfficientNetV2L(include_top=False)
base_model.trainable=False
input=tf.keras.layers.Input(shape=(224,224,3),name="inpute_layer")
#x=tf.keras.layers.experimental.preprocessing.Rescaling(1./255)(input)
# Pass the inputs to the base model
x=base_model(input)
#Average Pool the outputs of the base model(aggregate all the most important information,reduce number of computations)
x=tf.keras.layers.GlobalAveragePooling2D(name="global_avrage_pooling")(x)
# Create the inpute with the activation layer
outputs=tf.keras.layers.Dense(6,activation="softmax",dtype='float32',name="Output_layer")(x)
# Combine the inpute with output in mode
model_2=tf.keras.Model(input,outputs)
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/efficientnet_v2/efficientnetv2-l_notop.h5 473176280/473176280 [==============================] - 12s 0us/step
#complile the model
model_2.compile(loss=tf.keras.losses.categorical_crossentropy,
optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
metrics=['accuracy'])
model_2.summary()
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
inpute_layer (InputLayer) [(None, 224, 224, 3)] 0
efficientnetv2-l (Function (None, None, None, 1280 117746848
al) )
global_avrage_pooling (Glo (None, 1280) 0
balAveragePooling2D)
Output_layer (Dense) (None, 6) 7686
=================================================================
Total params: 117754534 (449.20 MB)
Trainable params: 7686 (30.02 KB)
Non-trainable params: 117746848 (449.17 MB)
_________________________________________________________________
history_model_2= model_2.fit(train_data,
steps_per_epoch=len(train_data),
epochs=10,
batch_size=32,
validation_data=test_data,
validation_steps=len(test_data),
callbacks=[checkpoint_callback]
)
Epoch 1/10 386/386 [==============================] - ETA: 0s - loss: 1.1502 - accuracy: 0.5589 Epoch 1: val_loss improved from inf to 1.00935, saving model to BigModel/checkpoint.ckpt 386/386 [==============================] - 153s 315ms/step - loss: 1.1502 - accuracy: 0.5589 - val_loss: 1.0093 - val_accuracy: 0.6124 Epoch 2/10 386/386 [==============================] - ETA: 0s - loss: 0.9743 - accuracy: 0.6214 Epoch 2: val_loss improved from 1.00935 to 0.94321, saving model to BigModel/checkpoint.ckpt 386/386 [==============================] - 116s 298ms/step - loss: 0.9743 - accuracy: 0.6214 - val_loss: 0.9432 - val_accuracy: 0.6311 Epoch 3/10 386/386 [==============================] - ETA: 0s - loss: 0.9220 - accuracy: 0.6413 Epoch 3: val_loss improved from 0.94321 to 0.91750, saving model to BigModel/checkpoint.ckpt 386/386 [==============================] - 122s 314ms/step - loss: 0.9220 - accuracy: 0.6413 - val_loss: 0.9175 - val_accuracy: 0.6385 Epoch 4/10 386/386 [==============================] - ETA: 0s - loss: 0.8892 - accuracy: 0.6530 Epoch 4: val_loss improved from 0.91750 to 0.90086, saving model to BigModel/checkpoint.ckpt 386/386 [==============================] - 115s 297ms/step - loss: 0.8892 - accuracy: 0.6530 - val_loss: 0.9009 - val_accuracy: 0.6482 Epoch 5/10 386/386 [==============================] - ETA: 0s - loss: 0.8679 - accuracy: 0.6594 Epoch 5: val_loss improved from 0.90086 to 0.88833, saving model to BigModel/checkpoint.ckpt 386/386 [==============================] - 116s 299ms/step - loss: 0.8679 - accuracy: 0.6594 - val_loss: 0.8883 - val_accuracy: 0.6559 Epoch 6/10 386/386 [==============================] - ETA: 0s - loss: 0.8481 - accuracy: 0.6670 Epoch 6: val_loss improved from 0.88833 to 0.87619, saving model to BigModel/checkpoint.ckpt 386/386 [==============================] - 116s 300ms/step - loss: 0.8481 - accuracy: 0.6670 - val_loss: 0.8762 - val_accuracy: 0.6666 Epoch 7/10 386/386 [==============================] - ETA: 0s - loss: 0.8359 - accuracy: 0.6745 Epoch 7: val_loss improved from 0.87619 to 0.86957, saving model to BigModel/checkpoint.ckpt 386/386 [==============================] - 116s 298ms/step - loss: 0.8359 - accuracy: 0.6745 - val_loss: 0.8696 - val_accuracy: 0.6637 Epoch 8/10 386/386 [==============================] - ETA: 0s - loss: 0.8211 - accuracy: 0.6795 Epoch 8: val_loss did not improve from 0.86957 386/386 [==============================] - 113s 291ms/step - loss: 0.8211 - accuracy: 0.6795 - val_loss: 0.8707 - val_accuracy: 0.6643 Epoch 9/10 386/386 [==============================] - ETA: 0s - loss: 0.8143 - accuracy: 0.6820 Epoch 9: val_loss improved from 0.86957 to 0.85921, saving model to BigModel/checkpoint.ckpt 386/386 [==============================] - 115s 297ms/step - loss: 0.8143 - accuracy: 0.6820 - val_loss: 0.8592 - val_accuracy: 0.6753 Epoch 10/10 386/386 [==============================] - ETA: 0s - loss: 0.8045 - accuracy: 0.6900 Epoch 10: val_loss did not improve from 0.85921 386/386 [==============================] - 113s 290ms/step - loss: 0.8045 - accuracy: 0.6900 - val_loss: 0.8608 - val_accuracy: 0.6724
plot_loss_curves(history_model_2)
model_2.load_weights(checkpoint_path)
<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x78a8ac774100>
model_save_path = 'efficient-2.h5'
model_2.save(model_save_path)
from tensorflow.keras.models import load_model
# Load the model
model_loaded2 = load_model('efficient-2.h5')
from tensorflow.keras.models import load_model
# Load the model
model_loaded = load_model('efficient.h5')
WARNING:tensorflow:Mixed precision compatibility check (mixed_float16): WARNING The dtype policy mixed_float16 may run slowly because this machine does not have a GPU. Only Nvidia GPUs with compute capability of at least 7.0 run quickly with mixed_float16. If you will use compatible GPU(s) not attached to this host, e.g. by running a multi-worker model, you can ignore this warning. This message will only be logged once WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.
making predictions
import os
import random
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.efficientnet import preprocess_input
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.models import load_model
# Base path to your dataset
base_path = '/home/archie/Music/ml2/FaceExpressions/'
# Class names (folders) to match your dataset structure
class_names = ['Ahegao', 'Angry', 'Happy', 'Neutral','Sad', 'Surprise']
# Function to prepare an image
def prepare_image(img_path, img_height=224, img_width=224):
"""Prepares an image for classification by the model."""
img = image.load_img(img_path, target_size=(img_height, img_width))
img_array = image.img_to_array(img)
img_array_expanded_dims = np.expand_dims(img_array, axis=0)
return preprocess_input(img_array_expanded_dims)
# Select 3 random images from each class and predict
for class_name in class_names:
folder_path = os.path.join(base_path, 'dataset', class_name)
images = os.listdir(folder_path)
selected_images = random.sample(images, 3)
for img_name in selected_images:
img_path = os.path.join(folder_path, img_name)
# Print the path of the current image
print(f"Image path: {img_path}")
prepared_img = prepare_image(img_path)
# Predict the class
prediction = model_loaded.predict(prepared_img)
predicted_class = class_names[np.argmax(prediction)]
# Display the image and prediction
img = plt.imread(img_path)
plt.imshow(img)
plt.title(f"True class: {class_name.capitalize()} \nPredicted class: {predicted_class.capitalize()}")
plt.axis('off')
plt.show()
Image path: /home/archie/Music/ml2/FaceExpressions/dataset/Ahegao/cropped_emotions.40209~ahegao.png 1/1 [==============================] - 19s 19s/step
Image path: /home/archie/Music/ml2/FaceExpressions/dataset/Ahegao/cropped_emotions.16964~ahegao.png 1/1 [==============================] - 4s 4s/step
Image path: /home/archie/Music/ml2/FaceExpressions/dataset/Ahegao/lol306~ahegao.png 1/1 [==============================] - 5s 5s/step
Image path: /home/archie/Music/ml2/FaceExpressions/dataset/Angry/cropped_emotions.231788~angry.png 1/1 [==============================] - 4s 4s/step
Image path: /home/archie/Music/ml2/FaceExpressions/dataset/Angry/967c722cc68bf384b492ebf7d731086a8e1b3ce226323e18fa779c79~angry.jpg 1/1 [==============================] - 4s 4s/step
Image path: /home/archie/Music/ml2/FaceExpressions/dataset/Angry/39bed989a5c51775d43c85a9cf25a8b64008eb111d3ec386ad12d497~angry.jpg 1/1 [==============================] - 4s 4s/step
Image path: /home/archie/Music/ml2/FaceExpressions/dataset/Happy/0c3502c7d27652cebd469863a9f19ace2ffe6c92656dadd4e7287d65.jpg 1/1 [==============================] - 5s 5s/step
Image path: /home/archie/Music/ml2/FaceExpressions/dataset/Happy/cropped_emotions.568851.png 1/1 [==============================] - 4s 4s/step
Image path: /home/archie/Music/ml2/FaceExpressions/dataset/Happy/2c65a273d56c6f366d7bc5d722b32b500b93675acc5990c9070a0b68.jpg 1/1 [==============================] - 4s 4s/step
Image path: /home/archie/Music/ml2/FaceExpressions/dataset/Neutral/cropped_emotions.452819f.png 1/1 [==============================] - 5s 5s/step
Image path: /home/archie/Music/ml2/FaceExpressions/dataset/Neutral/0b668067c6d1e90d81128ce524ddf8064633da6f031507f3f7655115f.jpg 1/1 [==============================] - 5s 5s/step
Image path: /home/archie/Music/ml2/FaceExpressions/dataset/Neutral/0c74e4d748038277e98ed0edb4283591743c5cc14130b25ebfe3cdf9f.jpg 1/1 [==============================] - 4s 4s/step
Image path: /home/archie/Music/ml2/FaceExpressions/dataset/Sad/5a1449e6afe27d23e1f4b6176c8ce4110d4af58700b4193457117cfb.jpg 1/1 [==============================] - 4s 4s/step
Image path: /home/archie/Music/ml2/FaceExpressions/dataset/Sad/cropped_emotions.499100.png 1/1 [==============================] - 4s 4s/step
Image path: /home/archie/Music/ml2/FaceExpressions/dataset/Sad/2c29b6319a374b0f67aa4d07bdc1a9e6fb7e83eb8f34707376e7479e.jpg 1/1 [==============================] - 4s 4s/step
Image path: /home/archie/Music/ml2/FaceExpressions/dataset/Surprise/cropped_emotions.261362~12fffff.png 1/1 [==============================] - 4s 4s/step
Image path: /home/archie/Music/ml2/FaceExpressions/dataset/Surprise/cropped_emotions.260485~12fffff.png 1/1 [==============================] - 4s 4s/step
Image path: /home/archie/Music/ml2/FaceExpressions/dataset/Surprise/cropped_emotions.266678~12fffff.png 1/1 [==============================] - 3s 3s/step
%%writefile app.py
import streamlit as st
import tensorflow as tf
import streamlit as st
@st.cache(allow_output_mutation=True)
def load_model():
model=tf.keras.models.load_model('/content/my_model2.hdf5')
return model
with st.spinner('Model is being loaded..'):
model=load_model()
st.write("""
# image Classification
"""
)
file = st.file_uploader("Please upload an brain scan file", type=["jpg", "png"])
import cv2
from PIL import Image, ImageOps
import numpy as np
st.set_option('deprecation.showfileUploaderEncoding', False)
def import_and_predict(image_data, model):
size = (180,180)
image = ImageOps.fit(image_data, size, Image.ANTIALIAS)
image = np.asarray(image)
img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
#img_resize = (cv2.resize(img, dsize=(75, 75), interpolation=cv2.INTER_CUBIC))/255.
img_reshape = img[np.newaxis,...]
prediction = model.predict(img_reshape)
return prediction
if file is None:
st.text("Please upload an image file")
else:
image = Image.open(file)
st.image(image, use_column_width=True)
predictions = import_and_predict(image, model)
score = tf.nn.softmax(predictions[0])
st.write(prediction)
st.write(score)
print(
"This image most likely belongs to {} with a {:.2f} percent confidence."
.format(class_names[np.argmax(score)], 100 * np.max(score))
)
Overwriting app.py
!pip install ngrok
Requirement already satisfied: ngrok in /usr/local/lib/python3.10/dist-packages (1.2.0)
!ngrok config add-authtoken 2eqKeuz9c2zS8dU2m1KpMCzb22i_6ZpX7JoLiMWfwXwCYy8v3
Authtoken saved to configuration file: /root/.config/ngrok/ngrok.yml
!pip install pyngrok
Collecting pyngrok Downloading pyngrok-7.1.6-py3-none-any.whl (22 kB) Requirement already satisfied: PyYAML>=5.1 in /usr/local/lib/python3.10/dist-packages (from pyngrok) (6.0.1) Installing collected packages: pyngrok Successfully installed pyngrok-7.1.6
from pyngrok import ngrok
!nohup streamlit run /content/app.py &
nohup: appending output to 'nohup.out'
!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip
!unzip ngrok-stable-linux-amd64.zip
--2024-04-09 01:58:03-- https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip Resolving bin.equinox.io (bin.equinox.io)... 54.237.133.81, 52.202.168.65, 18.205.222.128, ... Connecting to bin.equinox.io (bin.equinox.io)|54.237.133.81|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 13921656 (13M) [application/octet-stream] Saving to: ‘ngrok-stable-linux-amd64.zip’ ngrok-stable-linux- 100%[===================>] 13.28M 23.5MB/s in 0.6s 2024-04-09 01:58:04 (23.5 MB/s) - ‘ngrok-stable-linux-amd64.zip’ saved [13921656/13921656] Archive: ngrok-stable-linux-amd64.zip inflating: ngrok
!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip
--2024-04-09 01:58:20-- https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip Resolving bin.equinox.io (bin.equinox.io)... 54.237.133.81, 52.202.168.65, 18.205.222.128, ... Connecting to bin.equinox.io (bin.equinox.io)|54.237.133.81|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 13921656 (13M) [application/octet-stream] Saving to: ‘ngrok-stable-linux-amd64.zip.1’ ngrok-stable-linux- 100%[===================>] 13.28M 21.7MB/s in 0.6s 2024-04-09 01:58:21 (21.7 MB/s) - ‘ngrok-stable-linux-amd64.zip.1’ saved [13921656/13921656]
!mv ngrok /usr/local/bin/
!unzip ngrok-stable-linux-amd64.zip
Archive: ngrok-stable-linux-amd64.zip inflating: ngrok
!ls
app.py ngrok-stable-linux-amd64.zip 'EfficientNet_model_unfreezing_layers_Tanmay_Yuvraj (1).ipynb' ngrok-stable-linux-amd64.zip.1 efficientnetv2-l_notop.h5 nohup.out ngrok sample_data
!chmod +x ngrok
!pip install efficientnet
Collecting efficientnet
Downloading efficientnet-1.1.1-py3-none-any.whl (18 kB)
Collecting keras-applications<=1.0.8,>=1.0.7 (from efficientnet)
Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 50.7/50.7 kB 1.9 MB/s eta 0:00:00
Requirement already satisfied: scikit-image in /usr/local/lib/python3.10/dist-packages (from efficientnet) (0.19.3)
Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.10/dist-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (1.25.2)
Requirement already satisfied: h5py in /usr/local/lib/python3.10/dist-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (3.9.0)
Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (1.11.4)
Requirement already satisfied: networkx>=2.2 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (3.2.1)
Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (9.4.0)
Requirement already satisfied: imageio>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (2.31.6)
Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (2024.2.12)
Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (1.6.0)
Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (24.0)
Installing collected packages: keras-applications, efficientnet
Successfully installed efficientnet-1.1.1 keras-applications-1.0.8
!ngrok config add-authtoken 2eqKeuz9c2zS8dU2m1KpMCzb22i_6ZpX7JoLiMWfwXwCYy8v3
NAME:
ngrok - tunnel local ports to public URLs and inspect traffic
DESCRIPTION:
ngrok exposes local networked services behinds NATs and firewalls to the
public internet over a secure tunnel. Share local websites, build/test
webhook consumers and self-host personal services.
Detailed help for each command is available with 'ngrok help <command>'.
Open http://localhost:4040 for ngrok's web interface to inspect traffic.
EXAMPLES:
ngrok http 80 # secure public URL for port 80 web server
ngrok http -subdomain=baz 8080 # port 8080 available at baz.ngrok.io
ngrok http foo.dev:80 # tunnel to host:port instead of localhost
ngrok http https://localhost # expose a local https server
ngrok tcp 22 # tunnel arbitrary TCP traffic to port 22
ngrok tls -hostname=foo.com 443 # TLS traffic for foo.com to port 443
ngrok start foo bar baz # start tunnels from the configuration file
VERSION:
2.3.41
AUTHOR:
inconshreveable - <alan@ngrok.com>
COMMANDS:
authtoken save authtoken to configuration file
credits prints author and licensing information
http start an HTTP tunnel
start start tunnels by name from the configuration file
tcp start a TCP tunnel
tls start a TLS tunnel
update update ngrok to the latest version
version print the version string
help Shows a list of commands or help for one command
ERROR: Unrecognized command: config
!nohup streamlit run app.py &
nohup: appending output to 'nohup.out'
from pyngrok import ngrok
# Set the port number of your local server
port = 8501
# Start ngrok tunnel
ngrok_tunnel = ngrok.connect(port)
print("Public URL:", ngrok_tunnel.public_url)
# Keep the Colab session alive
input("Press Enter to stop the ngrok tunnel...")
ngrok.disconnect(ngrok_tunnel.public_url)
Public URL: https://c2e7-35-185-24-138.ngrok-free.app Press Enter to stop the ngrok tunnel...
WARNING:pyngrok.process.ngrok:t=2024-04-09T02:07:53+0000 lvl=warn msg="Stopping forwarder" name=http-8501-442dc45e-e713-4130-90b7-1ff2dc5d6980 acceptErr="failed to accept connection: Listener closed"